Prediction of Initial Emission Rates of 2-Butoxyethanol from Consumer Products Using Equilibrium Headspace Concentrations: An Application of the Vapor Pressure and Boundary Layer (VB) Model
Bibliographic record
Abstract
The initial emission rate of volatile organic compounds (VOCs) from consumer products is important for assessing potential human exposure to VOCs in products. The vapor pressure and boundary layer (VB) model developed in the past was used to predict the emission rates of VOCs in the fast decaying phase from petroleum-based wet materials. This study has extended the model to largely water-based products. Study results have shown a good agreement (ratio = 1.01, r2 = 0.89) between model-predicted initial emission rates (ER0) of 2-butoxyethanol (2-BE) based on its equilibrium headspace concentration and experimentally measured ER0 in a small dynamic environmental chamber for 20 consumer products. These water-based products included wood surface treating stains, general cleaning agents, and degreasers with 2-BE concentrations over a wide range. The results also demonstrated a dependency between the headspace concentrations of the target analytes and the water content in the liquid. But dependency on water content had no effect on the use of headspace concentration to predict the ER0. The ER0 of 2-BE in the products ranged from 100 to 3000 mg m(-2) h(-1). In the majority of cases, the 2-BE concentration range in individual products indicated in the Material Safety Data Sheet agreed with the measured data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".